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Category: Education

You’ve labored at the bench and generated data that you’re about to meticulously analyze before preparing the results of your hypothesis-testing for presentation. In this post, we’ll discuss elements that factor into making beautiful (and consistent) displays of data. View our recent post on Analysis Consistency in Flow Cytometry for a discussion of broader themes relating to analysis consistency.

To summarize what will follow in short: make sure all of your data are on scale, accurately compensated, and make sure all your plots are well-labeled.

Choosing plot types, appropriate statistics, and telling the full story

There are a number of plot types that can help you tell your story in different, visually pleasing ways when used appropriately. Among the flashier ways to display data are heatmaps, histograms, and histogram overlays. These one-dimensional representations owe their appeal largely to their ability to convey an easy-to-understand message: “This population changed in X amount in Y condition.” Where this gets tricky is if you’re trying to describe a heterogeneous population. When deciding on a plot type to use to convey your story, you’ll want to make sure you’re telling the whole story, and not omitting important information about the behavior of subsets in the course of eliminating a dimension of data display. In Cytobank, you can mouse over a heatmap square to display the underlying dot plot, which will reveal another dimension of information of your data.

When collecting and analyzing flow cytometry data, analysis consistency and quality control are essential in ensuring the validity of data within an experiment and among experiments carried out over time.

Quality control issues arise when there is variability in how experiments are carried out at the bench. We will tackle issues relating to data acquisition in a future post. In this post, we’ll discuss analysis-related quality concerns and introduce you to several Cytobank functionalities that are geared towards addressing these.

Where do issues surrounding quality control and analysis consistency arise?

– Multi-center endeavors to collect and analyze data
– Heads of labs who want to maintain consistency in analysis and presentation as scientists flux in and out of the lab
– Companies interested in a unified analysis and presentation style
– All scientists aiming to achieve reproducibility

Ever find yourself staring at a folder of FCS files and thinking, “Wait, now which tubes did I add PMA to, how much did I add, and which samples were these again?”

Jonathan from Cytobank/Stanford recommends what he calls “future proofing” in order to avoid this problem. He explained this approach during a CYTO 2011 Pre-Congress course in his talk titled “Flood Cytometry: Embracing Single Cell Systems Biology (and coping with large cytometry experiments).” In that talk, he outlined four easy steps that are useful for experiments of all sizes.

The ‘Cell Signaling’ course at LACI was taught by local instructors Nathalie Auphan-Anezin and Pierre Grenot, both of CIML, and Jonathan and Chris. The course led course participants through staining, collection, upload, and analysis of a phospho-flow experiment. We’ve briefly described the experiment here, made a version of the dataset public along with the original course protocol, and prepared a tutorial (part 1 and part 2) to lead you through Cytobank analysis of the course data.

Quantifying the percentage of cells expressing a protein of interest is a frequent goal in both basic research and clinical studies. Paired with per-cell comparisons of the level of protein expression, this approach provides a powerful way to track and immunophenotype populations of cells present in a particular sample.

One widely recognized application of flow cytometric immunophenotyping is determining the percentage of CD4+ cells in a gated lymphocyte population in order to determine prognosis for an HIV patient. Other applications include measuring a series of markers in order to distinguish between different forms of leukemia.

In Cytobank, you can use the “percent in gate” statistic to measure and display the percentage of cells in a selected gate as compared to each active population in your figure. To illustrate with a simple example, let’s examine a sample dataset looking at the percentage of CD25+ cells in a CD3+ T cell population.

Western blots are great. That is, unless you’re studying signal transduction in a mixed population of primary cells. The amount of material necessary for many biochemical techniques is usually not practical when working with patient samples or rare cell populations.

Flow cytometry to the rescue! Phospho-flow tracks biochemistry at the single cell level. Simultaneous assessment of cell surface markers and signaling events for individual cells enable biologists to profile disease samples or monitor the effects of drugs in clinical samples. More »